[lsy]

That's the difference though. I know my world model is fundamentally incomplete. Even more foundationally, I know that there is a world, and when my world model and the world disagree, the world wins. To a neural network there is no distinction. The closest the entire dynamic comes is the very basic annotation of RLHF which itself is done by an external human who is providing the value judgment, but even that is absent once training is over.

Despite not having the bird's sense for electromagnetic waves, I have an understanding that they are there, because humans saw behavior they couldn't describe and investigated, in a back-and-forth with a world that has some capacity to disprove hypotheses.

Additional modalities are really just reducible to more kinds of text. That still doesn't exhaust the world, and unless a machine has some ability to integrate new data in real time alongside a meaningful commitment and accountability to the world as a world, it won't be able to cope with the real world in a way that would constitute genuine intelligence.

[famouswaffles]

>I know my world model is fundamentally incomplete. Even more foundationally, I know that there is a world, and when my world model and the world disagree, the world wins.

Yeah this isn't really true. There's not how humans work. For a variety of reasons, Plenty stick with their incorrect model despite the world indicating otherwise. In fact, this seems to be normal enough human behaviour. Everyone does it, for something or the other. You are no exception.

And yes LLMs can in fact tell truth from fiction.

GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r

Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975

Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334

Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221

The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets - https://arxiv.org/abs/2310.06824

Your argument seems to boil down to "they can't perform experiments" but that isn't true either.

[user_named]

It is a very basic fact that LLMs have no concept of true or false, it only has an ability to look up what text data it has seen before. If you do not understand this you are in no position to discuss LLMs.

[SpicyLemonZest]

I really don't know what people mean when they say this. We routinely instruct computer chips to evaluate whether some condition is true and take action on that basis, even though the chip is "just" a selectively doped rock. Why would the details of an LLM's underlying architecture mean that it can't have a concept of true or false?

[xcv123]

One of the most ridiculous comments I have read about LLMs here.

The ~100 layer deep neural networks infer many levels of features over the text, including the concept of true and false. That is trivial for an LLM.

Are you completely unaware these are based on deep neural networks?

Convolutional Neural Networks don't operate by "look up" of text data.

[user_named]

Okay, so then tell me how does it decide whether it is true or false that Biden is the POTUS?

It's response is not based on facts about the world as it exists, but on the text data it has been trained on. As such, it is not able to determine true or false even if the response in the above example would be correct.

[jeffparsons]

Serious question, in pursuit of understanding where you're coming from: in what way do you think that your own reckoning is fundamentally different to or more "real" than what you're describing above?

I know I don't experience the world as it is, but rather through a whole bunch of different signals I get that give me some hints about what the real world might be. For example, text.

[Guvante]

You understand the concept of true vs false.

LLM does not, that isn't how it works.

You can say the difference is academic but there is a difference.

What is the difference between a real good faker of intelligence and actual intelligence is an open question.

But I will say most AI experts agree that LLM are not artificial general intelligence. It isn't just a lack of training data, they just are not of the category that we mean by that.

[edflsafoiewq]

In order to affirm something is true, you don't just need to know it, you need to know that you know it. LLMs fundamentally have no self-knowledge.

[user_named]

LLMs only knows it's text embeddings. It does not know the real world. Clear?

[ChatGTP]

It’s giving the most likely answer as opposed to the factual answer?

[xcv123]

> It's response is not based on facts about the world as it exists, but on the text data it has been trained on

How did you find out that Biden was elected if not through language by reading or listening to news? Do you have extra sensory perception? Psychic powers? Do you magically perceive "facts" without any sensory input or communication? Ridiculous.

By the same argument your knowledge is also not based on "facts" about the world, since you only learned about it by reading or listening. Absurd nonsense.

[user_named]

You didn't answer my question ergo you concede that LLMs don't know true or false.

[robbrown451]

It certainly doesn't "look up" text data it has seen before. That shows a fundamental misunderstanding of how this stuff works. That's exactly why I use the example above of Alpha Zero and how it learns to play Go, since that demonstrates very clearly that it's not just looking things up.

And I have no idea what you mean by saying that it has no concept of true or false. Even the simplest computer programs have a concept of true or false, that's kind of the simplest data type, a boolean. Large language models have a much more sophisticated concept of true and false that has a lot more nuance. That's really a pretty ridiculous thing to say.

[user_named]

Yes, you don't understand what I said. The model has no concept of true or false. It only has embeddings. If 'asked' a question it can see if that is consistent with its embeddings and probabilities or not. This is not a representation of the real world, of facts, but simply a product of its training.

[robbrown451]

"This is not a representation of the real world, of facts, but simply a product of its training."

Tell me how that doesn't apply to the human brain as well.

[jeffparsons]

They have no inherent concept of true or false, sure. But what are you comparing them to? It would be bold to propose that humans have some inherent concept of true or false in a way that LLMs do not; for both humans and LLMs it seems to be emergent.

[mattigames]

In all these arguments its implied that this "genuine intelligence" is something humans all have, and nothing could be farther from the truth, that is why we have flat earthers or religious people and many other people beliving for decades easily refutable lies.

[astrange]

There is no such thing as a world model, and you don't have one of them. This is a leftover bad psychological concept from the 70s AI researchers who never got anywhere. People and other creatures do very little modeling things, they mostly just do stuff.

[xcv123]

World model means inner representation of the external world. Any organism with a functioning brain has a world model. That's what brains do.

If you don't have a world model then you are a vegetable and could not be replying on HN.

[astrange]

If you close your eyes, how long can you navigate in the environment without hitting something? Not long, because you didn't model it.

If you're taking out the recycling, do you take the time to identify (model) each piece of it first? No, because that's not necessary.

[xcv123]

Wait, you actually think we are talking about modelling as a conscious deliberate process in active working memory? Well there's your fundamental mistake. That is not what we are discussing, not even remotely.

The vast model in your brain is learned and generated unconsciously without your direct awareness.

[astrange]

No, I didn't say anything about doing it consciously. Motion is largely unconscious, like how you can throw things at a target without thinking about it.

But if you're just using it to mean "factual memory", calling it modeling seems like false precision.

[xcv123]

Oh well in that case the answer is straightforward.

If you close your eyes and get lost after a few seconds, that's because that aspect of your model was not a 100% perfect exact replica of external reality that extended infinitely far in all spatial directions at all resolutions. For example, your internal spatial model is limited to some degree of accuracy and does not include the entire surface of Mars, but that doesn't mean that your model does not exist at all. Models are not perfect by definition. I thought this would be obvious.

Why would you think any model has to be a perfect exact 1:1 representation of the entire universe?

The model of reality in your head is a simplification that serves a purpose. Arbitrarily closing your fully functioning eyes is not something your model generating hardware was evolutionarily optimized for. Natural selection weeds out that kind of behaviour.

If you become blind then your model will change and optimize for other sensory inputs. Think of a blind man with a cane.

[jofla_net]

I do agree, but more importantly love this part of the argument! Its when all the personality differences become too much to bear and suddenly people are accused of not even knowing themselves. Been there before, what a wild ride!

[Dylan16807]

> suddenly people are accused of not even knowing themselves

It's not some desperate retort. People don't know themselves very well. Look at the research into confabulation, it seems to be standard operating procedure for human brains.